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  Subjects -> ELECTRONICS (Total: 207 journals)
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Annals of Telecommunications
Journal Prestige (SJR): 0.223
Citation Impact (citeScore): 1
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1958-9395 - ISSN (Online) 0003-4347
Published by Springer-Verlag Homepage  [2469 journals]
  • Editorial Expression of Concern: Motor imagery-based neuro-feedback system
           using neuronal excitation of the active synapses

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      PubDate: 2022-10-01
       
  • Traffic identification model based on generative adversarial deep
           convolutional network

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      Abstract: Abstract With the rapid development of network technology, the Internet has accelerated the generation of network traffic, which has made network security a top priority. In recent years, due to the limitations of deep packet inspection technology and port number-based network traffic identification technology, machine learning-based network traffic identification technology has gradually become the most concerned method in the field of traffic identification with its advantages. As the learning ability of deep learning in machine learning becomes more substantial and more able to adapt to highly complex tasks, deep learning has become more widely used in natural language processing, image identification, and computer vision. Therefore, more and more researchers are applying deep learning to network traffic identification and classification. To address the imbalance of current network traffic, we propose a traffic identification model based on generating adversarial deep convolutional networks (GADCN), which effectively fits and expands traffic images, maintains a balance between classes of the dataset, and enhances the dataset stability. We use the USTC-TFC2016 dataset as training and test samples, and experimental results show that the method based on GADCN has better performance than general deep learning models.
      PubDate: 2022-10-01
       
  • An improved user authentication and key agreement scheme for roaming
           service in ubiquitous network

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      Abstract: Abstract Up till now, numerous authentication and key agreement schemes have been proposed for ubiquitous networks. Recently, Arshad and Rasoolzadegan also proposed an authentication and key agreement scheme for ubiquitous network with user anonymity. However, we determined that Arshad and Rasoolzadegan’s scheme has the following flaws: (1) the login phase is inefficient, which may lead to server resource exhaustion attacks; (2) the password change phase is inefficient and not user-friendly; and (3) the revocation phase arisen when the mobile device is lost and the re-register phase is absent. Therefore, we propose an improved scheme that successfully removes all of the previous mentioned flaws existing in Arshad and Rasoolzadegan’s protocol by using the biometric based authentication. Formal analysis of the proposed scheme is conducted using the random oracle model, and heuristic analysis is also conducted to demonstrate that the proposed scheme fulfills all of the security requirements. In addition, the proposed scheme is validated by the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, computational and communication cost comparisons indicate that our improved scheme is more suitable for ubiquitous networks.
      PubDate: 2022-10-01
       
  • Block-wise Kaczmarz successive interference cancellation: a matrix
           algebraic approach

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      Abstract: Abstract In this work, a novel block-wise Kaczmarz successive interference cancellation (BKSIC) detector, that is, a generalization of the Kaczmarz SIC detector proposed recently, is presented. Two reduced complexity versions of this detector are also detailed. This new detector is then analyzed using a matrix algebraic approach and shown that it is in fact a matrix filtering of the received signal, which allows the derivation of a closed form expression for its BER performance. Its convergence behavior is studied and the conditions of its convergence are determined. Simulation results indicate that the proposed block-wise SIC detector not only reduces significantly the detection delay but also achieves better BER performance.
      PubDate: 2022-10-01
       
  • Oddlab: fault-tolerant aware load-balancing framework for data center
           networks

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      Abstract: Abstract Data center networks (DCNs) act as critical infrastructures for emerging technologies. In general, a DCN involves a multi-rooted tree with various shortest paths of equal length from end to end. The DCN fabric must be maintained and monitored to guarantee high availability and better QoS. Traditional traffic engineering (TE) methods frequently reroute large flows based on the shortest and least-congested paths to maintain high service availability. This procedure results in a weak link utilization with frequent packet reordering. Moreover, DCN link failures are typical problems. State-of-the-art approaches address such challenges by modifying the network components (switches or hosts) to discover and avoid broken connections. This study proposes Oddlab (Odds labels), a novel deployable TE method to guarantee the QoS of multi-rooted data center (DC) traffic in symmetric and asymmetric modes. Oddlab creatively builds a heuristic model for efficient flow scheduling and faulty link detection by exclusively using the gathered statistics from the DCN data plane, such as residual bandwidth and the number of installed elephant flows. Besides, the proposed method is implemented in an SDN-based DCN without altering the network components. Our findings indicate that Oddlab can minimize the flow completion time, maximize bisection bandwidth, improve network utilization, and recognize faulty links with sufficient accuracy to improve DC productivity.
      PubDate: 2022-10-01
       
  • Performance evaluation of an HTTP proxy implemented as a virtual network
           function

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      Abstract: Abstract Network functions virtualization (NFV) is an important approach in the telecommunications industry. One of the main features of NFV is the execution of network functions in software rather than using specific hardware. These functions can run on virtualization platforms, which can increase service elasticity and reduce infrastructure costs. However, virtualization imposes performance penalties, which can severely impact NFV services. In this work, we analyze this performance impact when the virtualized network function is an HTTP proxy. We then compare two virtualization solutions, i.e., KVM and Docker, under different configurations. Our results show that Docker containers yield performance close to that of native Linux for HTTP proxies since Docker does not employ a hypervisor. We show that KVM incurs a severe performance penalty, which a paravirtualization approach can reduce. We also evaluate how much load balancing in Docker can improve the performance of virtual proxies. We show that, for our scenario, two parallel proxies significantly improve performance. However, we observe a negative impact when increasing the number of proxies since they interfere with each other.
      PubDate: 2022-10-01
       
  • Full data rate space-time code selection

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      Abstract: Abstract This paper proposes a full-rate space-time code selection technique for multiple transmitter antenna diversity systems. The technique uses orthogonal space-time codes for transmitting with two antennas and a quasi-orthogonal space-time code when transmitting with four antennas. We selected the space-time code and the transmitter antennas by comparing the equivalent SISO channels with a set of predetermined threshold levels, found off-line as a function of the Doppler frequency. The performance of the proposed technique was evaluated using the error rate and the spectral efficiency, outperforming other existing techniques.
      PubDate: 2022-10-01
       
  • Maximizing the underwater wireless sensor networks’ lifespan using BTC
           and MNP5 compression techniques

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      Abstract: Abstract The sending/receiving of data is the biggest energy user in the underwater wireless sensor networks (UWSNs). The energy supplied by the battery is the most critical resource in the sensor node affecting UWSN’s lifetime. At sensor nodes, energy is used in several forms, such as data reception and transmission, sensing, and processing. In all these, the transmission of data is very expensive in terms of power depletion, whereas data processing demand is known to be much smaller. Therefore, to save energy and boost the lifespan of UWSN, it is crucial to reduce data sending/receiving. In this paper, a two-level data compression method is proposed to work at two levels of the network that are sensor nodes and the gateway. At the sensor nodes level, we introduced a compression-based block truncation coding (CBBTC) strategy to minimize the amount of transferred data, reduce the energy used, and thereby prolong the network lifetime while attempting to keep the accuracy of the data reaching the base station at the best possible level. At the gateway (i.e., cluster head (CH)) level, a lossless compression algorithm called MNP5 is proposed to compress the obtained data sets. The MNP5 method is a double-staged procedure that consists of run-length (RLE) and adaptive frequency encodings. Using extensive simulation experiments, the assessment of proposed approaches is performed. Compared to prefix frequency filtering (PFF) and Harb protocols, the results of the simulation prove effectiveness, i.e., overhead reduction of up to 98% in residual data and 98% in energy consumption while preserving the accuracy of sent data above 90%.
      PubDate: 2022-10-01
       
  • A study on robustness of malware detection model

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      Abstract: Abstract In recent years, machine learning–based techniques are used to prevent cyberattacks caused by malware, and special attention is paid to the risks posed by such systems. However, there are relatively few studies on adversarial attacks on machine learning–based malware detection model using portable execution (PE) surface information and even less study from a defender’s perspective. In this study, we focus on malware detection field and treat the aforementioned issue from the perspectives of both attackers and defenders; subsequently, we propose a novel black-box adversarial attack method, named Image_Resource attack, and a robust malware detection model, respectively, using dimensionality reduction and machine learning techniques. The robustness of the proposed model is evaluated using PE surface information obtained from the FFRI Dataset 2018. During robustness evaluation, distances (e.g., the Euclidean distance) between the malware and benign files are measured, and the effectiveness of Image_Resource attack is estimated. Thus, we establish the effectiveness and superiority of the proposed model in terms of detection accuracy and robustness.
      PubDate: 2022-10-01
       
  • MIMO-OFDM LTE system based on a parallel IFFT/FFT on NoC-based FPGA

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      Abstract: Abstract The growing demand for wireless devices capable of performing complex communication processes has imposed an urgent need for high-speed communication systems and advanced network processors. This paper proposes a hardware workflow developed for the Long-Term Evolution (LTE) communication system. It studies the multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) LTE system. Specifically, this work focuses on the implementation of the OFDM block that dominates the execution time in high-speed communication systems. To achieve this goal, we have proposed an NoC-based low-latency OFDM LTE multicore system that leverages Inverse Fast Fourier Transform (IFFT) parallel computation on a variable number of processing cores. The proposed multicore system is implemented on an FPGA platform using the ProNoC tool, an automated rapid prototyping platform. Our obtained results show that LTE OFDM execution time is drastically reduced by increasing the number of processing cores. Nevertheless, the NoC’s parameters, such as routing algorithm and topology, have a negligible influence on the overall execution time. The implementation results show up to 24% and 76% execution time reduction for a system having 2 and 16 processing cores compared to conventional LTE OFDM implemented in a single-core, respectively. We have found that a 4×4 Mesh NoC with XY deterministic routing connected to 16 processing tiles computing IFFT task is the most efficient configuration for computing LTE OFDM. This configuration is 4.12 times faster than a conventional system running on a single-core processor.
      PubDate: 2022-10-01
       
  • Vertical group handover congestion game for a vehicular platoon in VLC
           networks

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      Abstract: Abstract Vehicle platooning systems improve road active safety while reducing vehicular congestion. Visible light communications (VLC) is an efficient technology that enables safety messages exchange between platoon members, and thus improves road safety. Nevertheless, packet transmission could be perturbed by neighboring vehicles, impacting the quality of service and inducing packet loss. Therefore, it is of paramount importance to tackle the provisioning of a vertical handover, of a group of competing platoon vehicles, to a radio frequency (RF) technology. This paper models the network selection procedure by means of a congestion game with an unknown number of players. More precisely, the game considers players that aim at choosing resources among a certain number of resources. Moreover, the game considers the lack of information about the users’ preferences. The main objective is to distribute platoon vehicles among available RF technologies, while maximizing the throughput of each user and avoiding network congestion. The game is solved with the safety-level equilibrium. Performance analysis shows that our solution offers high throughput, low delay, and packet loss ratio for each platoon vehicle while achieving load balancing among available networks.
      PubDate: 2022-10-01
       
  • Cloud assisted semi-static secure accountable authority identity-based
           

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      Abstract: Abstract Cloud computing has gained widespread popularity in the industry and academia and rapidly becomes an integral part of our everyday life. It offers several benefits including reduced cost on technical support for data backups, saving electric power and maintenance cost. These encourage the major industry players like Google, IBM, Microsoft to invest into cloud storage with the goal to extend the spectrum of cloud-based services from open public to closed private. One of the crucial challenges in cloud computing is the security of outsourced data. Sharing sensitive data among multiple users under the same domain in a secure and efficient way requires technical solutions. Identity-based broadcast encryption (IBBE) is an important building block in cryptography. This is a one to many encryption that broadcasts a message to many identities. In this paper, we address the key escrow problem of IBBE. As private key generator (PKG) generates secret keys for users, it has the capability to decrypt the ciphertext and recover the message. The accountable authority IBBE was introduced to give accountability in IBBE, where white-box A-IBBE can differentiate the creator of a given pirated private key between the PKG and suspected user and black-box A-IBBE can further trace the creator of a decoder box. In our construction, we have established the secret key by using zero-knowledge proof between the user and PKG. The decryption key is held by the user only. This restricts PKG to re-distribute keys maliciously and solves the key escrow problem. Inspired by the work of Zhao et al., we develop an accountable authority identity-based broadcast encryption scheme (A-IBBE). Our construction is the first publicly traceable weak black-box A-IBBE scheme secure against the indistinguishability under chosen-identity and chosen-plaintext attack in the standard model. We support the conjectured security of our candidate by analysis and prove its security without using any random oracle under the hardness of the decision bilinear Diffie-Hellman exponent (DB-DHE) sum problem. Another interesting feature of our scheme is that it features a constant size secret key and ciphertext. More positively, when contrasted with the existing similar schemes, our scheme exhibits favorable results in terms of secret key size and ciphertext length with constant number of pairing computations.
      PubDate: 2022-09-20
       
  • Sliding window symbol-pair constrained codes for energy harvesting

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      Abstract: Abstract The binary symbol-pair constrained codes that can enable simultaneous transfer of information and energy is the topic of interest in this paper. The construction and properties of such binary symbol-pair code using the sliding window constraint are discussed in this paper. The sliding window constraint ensures the presence of at least t weighted symbols within any prescribed window of l consecutive symbol-pairs. The information capacity of (l,t)-constrained sequences has been obtained and analyzed. This paper provides the block code construction of (l,t) symbol-pair constrained codes of length n without using a n-step finite-state machine. The information capacity obtained in this paper is better than the information capacity of (l,t)-constrained codes in Schouhamer Immink and Kui (IEEE Commun Lett 24(9):1890–1893, 2020) [16].
      PubDate: 2022-09-10
       
  • Joint energy efficiency and load balancing optimization in hybrid IP/SDN
           networks

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      Abstract: Abstract Software-defined networking (SDN) is a paradigm that provides flexibility and programmability to computer networks. By introducing SDN nodes in a legacy IP network topology, network operators can benefit on higher control over the infrastructure. However, this migration is not a fast or straightforward process. Furthermore, to provide an adequate quality of service in hybrid IP/SDN networks, the coordination of both IP and SDN paradigm is fundamental. In this paper, this coordination is used to solve two optimization problems that are typically solved separately: (i) traffic load balancing and (ii) power consumption minimization. Each of these problems has opposing objectives, and thus, their joint consideration implies striking a balance between them. Therefore, this paper proposes the Hybrid Spreading Load Algorithm (HSLA) heuristic that jointly faces the problems of balancing traffic by minimizing link utilization and network’s power consumption in a hybrid IP/SDN network. HSLA is evaluated over differently sized topologies using different methods to select which nodes are migrated from IP to SDN. These evaluations reveal that alternative approaches that only address one of the objectives are outperformed by HSLA.
      PubDate: 2022-09-08
       
  • Multipath neural networks for anomaly detection in cyber-physical systems

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      Abstract: Abstract An Intrusion Detection System (IDS) is a core element for securing critical systems. An IDS can use signatures of known attacks, or an anomaly detection model for detecting unknown attacks. Attacking an IDS is often the entry point of an attack against a critical system. Consequently, the security of IDSs themselves is imperative. To secure model-based IDSs, we propose a method to authenticate the anomaly detection model. The anomaly detection model is an autoencoder for which we only have access to input-output pairs. Inputs consist of time windows of values from sensors and actuators of an Industrial Control System. Our method is based on a multipath Neural Network (NN) classifier, a newly proposed deep learning technique for which we provide an in-depth description. The idea is to characterize errors of an IDS’s autoencoder by using a multipath NN’s confidence measure c. We use the Wilcoxon-Mann-Whitney (WMW) test to detect a change in the distribution of the summary variable c, indicating that the autoencoder is not working properly. We compare our method to two baselines. They consist in using other summary variables for the WMW test. We assess the performance of these three methods using simulated data. Among others, our analysis shows that: 1) both baselines are oblivious to some autoencoder spoofing attacks while 2) the WMW test on a multipath NN’s confidence measure enables detecting eventually any autoencoder spoofing attack.
      PubDate: 2022-08-31
       
  • Helix Multi-layered: a context broker federation for an efficient
           cloud-to-things continuum

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      Abstract: Abstract Next-generation networks offer a flexible virtualized infrastructure that aligns back-end platforms for the Internet of Things with the management and orchestration (MANO) platforms. Together, they can synchronously support and improve application performance while establishing a cloud-to-things continuum. This study examines the architecture of the Helix Multi-layered technology platform designed for the management and distributed processing of context information through horizontal and vertical data federation techniques through a multi-level approach. We set out the architecture of the Helix Multi-layered platform with regard to its internal components, operating modes, and steps of its integration process, together with the 5th Generation (5G) mobile network infrastructure mediated by the Novel Enablers for Cloud Slicing (NECOS), a Lightweight Slice-Defined Cloud (LSDC) platform. The integration of NECOS and Helix Multi-layered platforms is examined in a test laboratory. In the article, experiments are carried out to compare the internal processing time of Helix Broker with that of Orion Context Broker, FIWARE’s main context broker. An assessment is made of the performance of some operating modes with the aim of achieving low latency in communication between the devices located at the edge. The experiments showed One-Way-Delay (OWD) values close to those of the Ultra-Reliable Low Latency Communications (uRLLC) requirements that are expected in 5G mobile networks.
      PubDate: 2022-08-25
       
  • Forensic investigation of Cisco WebEx desktop client, web, and Android
           smartphone applications

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      Abstract: Abstract Digital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. We focus on three digital forensic areas, namely memory, disk space, and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different data localities. These include user credentials, emails, user IDs, profile photos, chat messages, shared media, meeting information including meeting passwords, contacts, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and call logs. We develop a memory parsing tool for Cisco WebEx based on the extracted artifacts. Additionally, we identify anti-forensic artifacts such as deleted chat messages. Although network communications are encrypted, we successfully retrieve useful artifacts such as IPs of server domains and host devices along with message/event timestamps.
      PubDate: 2022-08-12
       
  • EC-MOPSO: an edge computing-assisted hybrid cluster and MOPSO-based
           routing protocol for the Internet of Vehicles

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      Abstract: Abstract In recent years, the Internet of Vehicles (IoV) has received a lot of attention due to its unique features, such as rapid topology change, specific movement patterns, and variable node density and speed. Providing an efficient routing algorithm is one of the main challenges of these networks. Roadside units (RSU) defined in vehicular Ad hoc network (VANET) architecture can play as an edge computing device and assist in the routing process. They are usually fixed along the roadside or in specific locations such as junctions. On the other hand, bioinspired metaheuristic optimization algorithms are good candidates for this field due to their dynamic nature and ability to consider several parameters simultaneously. Clustering can also be used to reduce complexity. In this paper, an edge computing-assisted cluster-based routing algorithm utilizing multi-objective particle swarm optimization (MOPSO), named EC-MOPSO, is presented for the IoV applications. The proposed method evaluates different particles based on the three objective functions of accumulative delay, number of hops, and number of cluster members. Particles suggesting lower delays, fewer hops, and more members in the same cluster are considered to be superior. RSUs (edges) are responsible for the optimization procedure. Evaluation results show that the proposed method has a significant advantage over similar works in terms of distance traveled, number of hops, latency, packet delivery ratio, and convergence time.
      PubDate: 2022-08-01
       
  • A statistical analysis of intrinsic bias of network security datasets for
           training machine learning mechanisms

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      Abstract: Abstract Machine learning mechanisms for network intrusion detection systems lack accurate evaluation, comparison, and deployment due to the scarcity of well-constructed datasets. In this paper, we propose a statistical analysis of the features contained in four highly used security datasets. We conclude that the analyzed datasets should not be used as a benchmark for creating novel anomaly-based mechanisms for intrusion detection systems. The analyzed datasets introduce a biased classification since features are over-correlated, and most of the features are capable of making a complete distinction between normal and attack flows. Our proposed methodology analyzes the correlation among features instead of checking for redundant values or data imbalance. The results align with the performance of three machine learning techniques. We show that biased classification occurs due to a significant difference between attack and normal data. The syntactically generated features are statistically different between normal and attack classes, which implies overfitting in the machine learning approaches.
      PubDate: 2022-08-01
       
  • Enabling technologies for running IoT applications on the cloud

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      PubDate: 2022-07-09
      DOI: 10.1007/s12243-022-00918-7
       
 
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